---
license: apache-2.0
library_name: transformers
tags:
- generated_from_trainer
base_model: google/gemma-2b
model-index:
- name: out1v
  results: []
pipeline_tag: text-generation
---

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
# use google/gemma-7b if you have access
base_model: google/gemma-2b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
#lora_model_dir: 
load_in_8bit: false
load_in_4bit: true
strict: false

# huggingface repo
datasets:
  - path: ./python-oasst/cleaned-dataset.jsonl
    type: oasst
val_set_size: 0.20
output_dir: ./out1v

adapter: lora
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
gptq: false
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true

wandb_project: gemma-2b-it
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:


gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: true
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# out1v

This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co./google/gemma-2b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1243

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1045
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.9865        | 0.0   | 1     | 2.2808          |
| 1.1033        | 0.25  | 2613  | 1.1338          |
| 1.0035        | 0.5   | 5226  | 1.1308          |
| 1.0089        | 0.75  | 7839  | 1.1272          |
| 0.9816        | 1.0   | 10452 | 1.1238          |
| 0.8727        | 1.25  | 13065 | 1.1270          |
| 0.9951        | 1.5   | 15678 | 1.1254          |
| 1.01          | 1.75  | 18291 | 1.1242          |
| 1.0677        | 2.0   | 20904 | 1.1243          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0